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'''Image Quality.''' We measure LPIPS (Zhang et al., [https://arxiv.org/html/2408.14837v1#bib.bib40 2018]) and PSNR using the teacher-forcing setup described in Section [https://arxiv.org/html/2408.14837v1#S2 2], where we sample an initial state and predict a single frame based on a trajectory of ground-truth past observations. When evaluated over a random holdout of 2048 trajectories taken in 5 different levels, our model achieves a PSNR of <math>29.43</math> and an LPIPS of <math>0.249</math>. The PSNR value is similar to lossy JPEG compression with quality settings of 20-30 (Petric & Milinkovic, [https://arxiv.org/html/2408.14837v1#bib.bib22 2018]). Figure [https://arxiv.org/html/2408.14837v1#S5.F5 5] shows examples of model predictions and the corresponding ground truth samples.

Image Quality. We measure LPIPS (Zhang et al., 2018) and PSNR using the teacher-forcing setup described in Section 2, where we sample an initial state and predict a single frame based on a trajectory of ground-truth past observations. When evaluated over a random holdout of 2048 trajectories taken in 5 different levels, our model achieves a PSNR of $29.43$ and an LPIPS of $0.249$ . The PSNR value is similar to lossy JPEG compression with quality settings of 20-30 (Petric & Milinkovic, 2018). Figure 5 shows examples of model predictions and the corresponding ground truth samples.

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	'''Image Quality.''' We measure LPIPS (Zhang ~~et al~~., [https://arxiv.org/html/2408.14837v1#bib.bib40 2018]) and PSNR using the teacher-forcing setup described in Section [https://arxiv.org/html/2408.14837v1#S2 2], where we sample an initial state and predict a single frame based on a trajectory of ground-truth past observations. When evaluated over a random holdout of 2048 trajectories taken in 5 different levels, our model achieves a PSNR of <math>29.43</math> and an LPIPS of <math>0.249</math>. The PSNR value is similar to lossy JPEG compression with quality settings of 20-30 (Petric & Milinkovic, [https://arxiv.org/html/2408.14837v1#bib.bib22 2018]). Figure [https://arxiv.org/html/2408.14837v1#S5.F5 5] shows examples of model predictions and the corresponding ground truth samples.		'''Image Quality.''' We measure LPIPS (Zhang et al., [https://arxiv.org/html/2408.14837v1#bib.bib40 2018]) and PSNR using the teacher-forcing setup described in Section [https://arxiv.org/html/2408.14837v1#S2 2], where we sample an initial state and predict a single frame based on a trajectory of ground-truth past observations. When evaluated over a random holdout of 2048 trajectories taken in 5 different levels, our model achieves a PSNR of <math>29.43</math> and an LPIPS of <math>0.249</math>. The PSNR value is similar to lossy JPEG compression with quality settings of 20-30 (Petric & Milinkovic, [https://arxiv.org/html/2408.14837v1#bib.bib22 2018]). Figure [https://arxiv.org/html/2408.14837v1#S5.F5 5] shows examples of model predictions and the corresponding ground truth samples.